There has been extended debate about how to conceptualise inter‐organizational restructuring in late twentieth century capitalism, giving rise to a number of models that attempt to represent productive change. A number of such conceptualisations of transformation under the banner of “agility” attempt to provide guidance about “managing” physical and social relationships within and between companies in response to growing market complexity. The theoretical argument in this paper is that inter‐firm agility cannot be objectively understood in all cases using simple unidirectional cause and effect as such theories do not take into account more subjective aspects of interaction. Specifically, we argue that to have a vision of agility in action there must be an evaluation of complexity in and between organisational boundaries with a theoretical approach that gives a more robust appreciation of inter‐firm ties. Conceptualising agility in this way captures the essence of tacit knowledge between firms along with the physical dynamics of network functioning.

Introduction The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking. Methods Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review. Results Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour. Conclusions and implications FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally better than 80%. The models implemented (and compared) for classification were: Logistic Regression, Quadratic Discriminant Analysis, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest and Multi-layer Perceptron (Neural Network). Postcode data was then used to classify solely on demographics derived from the UK Land Registry and similar public data sources. Prediction accuracy remained better than 60%.

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In this article, we outline the concept of knowledge infrastructure and describe how it differs from Information Technology (IT) infrastructure, with particular regard to the implications for education theory, practice and policy. We examine the inherent limits to growth for attempts to handle knowledge, as opposed to information, via the types of software and hardware likely to be available in the next few decades. We show how a simple process model can be used to identify pinch points where knowledge, as opposed to information, is a bottleneck. We also show how a simple model of knowledge types and knowledge locations can be combined with the process model to remove those bottlenecks via existing low-cost technology and a more efficient use of existing human expertise. We conclude that a minimal investment in knowledge infrastructure would provide significant human, social and economic benefits, by creating major added value from existing digital and organisational infrastructure.